A startup real estate brokerage uses a mix of savvy and stats to pair customers and apartments.

While the Internet’s made it easier to browse and search real estate options, anyone who’s shopped for a house or apartment knows many brokerages haven’t changed their practices much since the days of paper listings–finding the perfect place hasn’t gotten much easier.

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Urban Compass, a New York-based brokerage poised to expand to other markets after announcing a $40 million round of funding earlier this month, plans to use that fact to its advantage, using a mixture of data science and old-fashioned broker know-how to match clients to brokers, neighborhoods, and homes.

The brokerage says its website contains up-to-date listings that match actual inventory, along with online neighborhood guides that combine hard figures on rents and commute times with photos and frank descriptions.

And once customers decide they’re ready to look at a particular property or enlist the help of an Urban Compass agent, an ever-evolving algorithm pairs them with a broker based on their interests, desired price range, and other factors.

“We try to turn around the way that traditional real estate companies allocate leads,” Stern says, explaining that since listings aren’t the exclusive domain of particular agents, clients don’t have to wait for the right person to become available to see the property they’re interested in.

“What happens if that agent is on the phone or showing another client a property?” he asks. “That client never gets to see the property, because there’s sort of a one-to-one relationship between the listing and the listing agent.”

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Urban Compass doesn’t steer clients to particular agents based on income or other suspect criteria, he emphasizes, but aims to pair clients with agents who know the neighborhoods and amenities they’re interested in.

“We look at the behavior of not only our agents over time–deals that they’re closing and people that they’re able to help,” he says.

Once a client’s working with an agent, the company relies on a mixture of automated search and matching tools and the broker’s own knowledge and experience to help find the right properties, says Stern.

“Agents always have that je ne sais quoi–that ability to see almost nonlogical links between neighborhoods or building styles or stuff like that,” he says.

And to reach out to prospective customers and pick listings to highlight on a first web visit, the company uses a variety of criteria to try to predict what house hunters want.

“We like to do it a little smarter than the traditional postcard in the mail,” he says. “The brains-over-brawn approach, we like to call it.”

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Urban Compass can use browsers’ geographical locations to make an educated guess at what properties might interest them and can notify contacts when an interesting listing sees a price drop, says Stern.

“Certain listing attract international buyers, for example,” he says. “Certain listings that provide more luxury amenities or more integrated amenities are more interesting to people that are relocating from elsewhere in the states.”

Visitors from college campuses are often interested in certain cities, he says: Students from engineering schools might be more interested in properties in Austin or San Francisco, while schools with more business majors might send more new grads to New York and Chicago, though he says an actual matching formula would be involve more complicated factors.

Stern says he’s optimistic the company’s mix of tech and talent will serve them well as they expand into new markets.

“The search experience that we provide is infinitely scalable — you just have to fill it with different geographies and different listings,” he says. “I think the value proposition has nothing to do with geography and everything to do with the home search, which is pretty ubiquitous around the world.”